Rate-Perturbing Single Amino Acid Mutation for Hydrolases: A Statistical Profiling

Abstract

Hydrolases are a critical component for modern chemical, pharmaceutical, and environmental sciences. Identifying mutations that enhance catalytic efficiency presents a roadblock to design and to discover new hydrolases for broad academic and industrial uses. Here, we report the statistical profiling for rateperturbing mutant hydrolases with a single amino acid substitution. We constructed an integrated structure−kinetics database for hydrolases, IntEnzyDB, which contains 3907 k cat s, 4175 K M s, and 2715 Protein Data Bank IDs. IntEnzyDB adopts a relational architecture with a flattened data structure, enabling facile and efficient access to clean and tabulated data for machine learning uses. We conducted statistical analyses on how single amino acids mutations influence the turnover number (i.e., k cat ) and efficiency (i.e., k cat /K M ), with a particular emphasis on profiling the features for rate-enhancing mutations. The results show that mutation to bulky nonpolar residues with a hydrocarbon chain involves a higher likelihood for rate acceleration than to other types of residues. Linear regression models reveal geometric descriptors of substrate and mutation residues that mediate rate-perturbing outcomes for hydrolases with bulky nonpolar mutations. On the basis of the analyses of the structure−kinetics relationship, we observe that the propensity for rate enhancement is independent of protein sizes. In addition, we observe that distal mutations (i.e., >10 Å from the active site) in hydrolases are significantly more prone to induce efficiency neutrality and avoid efficiency deletion but involve similar propensity for rate enhancement. The studies reveal the statistical features for identifying rate-enhancing mutations in hydrolases, which will potentially guide hydrolase discovery in biocatalysis.

IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database

“…The breadth of the distribution is wider than that of hydrolases (i.e., −4.2 to 9.4 kcal/mol), but the mean value is similar (i.e., 1.2 kcal/mol). 32 We categorized the mutants to be efficiency-enhancing (ΔΔG ‡ ≤ -0.5 kcal/mol), -neutral (ΔΔG ‡ > -0.5 and ≤ 0.5kcal/mol, and -deleterious (ΔΔG ‡ > 0.5 kcal/mol). We observed 11.2% of the mutants to be efficiency-enhancing, 29.5% neutral, and 59.3% deleterious.…”

“…After joining enzyme kinetics with structure data using the reference table (Figure 1), we obtained 1019 one-to-one mapped enzyme structure-kinetics pairs, including 376 oxidoreductases, 95 transferases, 313 hydrolases, 119 ligases, 76 isomerases, and 40 lyases. Noticeably, the data entries for hydrolase (313) is less than the amount of data (i.e., 403) curated in our prior work 32 . This is because in this work, we applied a stricter filtration condition that traces every kinetic entry to the corresponding structure in literature using text mining (detailed in the Computational Method section) rather than simply relied on UniprotKB to map kinetic entry with the best-resolved structure as done previously.…”

“…We have previously reported the beta-version of IntEnzyDB as a hydrolase database. 32 In this work, we expanded IntEnzyDB to incorporate data from six enzyme commission classes. IntEnzyDB allows fast operation of large amount of enzyme structure data and enables mapping between enzyme kinetics and structure.…”

EnzyHTP: A High-Throughput Computational Platform for Enzyme Modeling

“…A computational high-throughput platform parallelizes the computation of a large number of enzyme models, which allows understanding of enzyme catalytic mechanisms across a large number enzymes in a protein family, enables the virtual screening of enzyme mutants, and collects electronic structure and dynamics data for building data-driven predictive models. 20 Computational workflows have been established to automate general-purpose protein simulations. For example, Doerr et al developed HTMD 21 for high-throughput molecular dynamics (MD) simulations with the ACEMD 22 in a protein superfamily combining MD and template-based structural prediction.…”

ARMer: A Python Library for Adaptive Resource Allocation in High-Throughput Workflow

“…With the advent of machine learning, these workflows allow facile collection of molecular features, or even provide high-accuracy computational data for model training. [19][20][21][22] Boosted by advances of computer hardware and software, high-throughput molecular modeling workflows enable the operation of large amount and diverse types of computational tasks.…”